AI in marketing: we need more analysis, not analytics

Within the next five years, we believe machine learning will be built into marketing software and processes as standard and, most importantly, become an everyday skill that marketers are expected to possess. The challenge for marketers today is in overcoming the mysticism that surrounds machine learning and AI, to understand what it might mean to them.

It helps to start by looking at one of the main issues we marketers face – data. Too much of it and in too many places. Right now, my phone is creating data about my location, my Apple watch is tracking how active I am, and my laptop and its applications are tracking the work I do. Each is creating data which will no doubt be used to sell to me, and people like me. Times that by millions of people and millions of moments and you begin to get a sense of the amount of data being created all the time.

So, how can machine learning help us make sense of this data?

Let’s first examine the different kinds of decisions people make, with help from the Cynefin framework (pronounced Kuh-nevin), developed by IBM in the 90s.

This tells us that there are five types of decision – simple, complicated, complex, chaotic and disordered. We’re going to look at the first four of these.

Simple decisions are predictable, process-oriented and best practice decisions that require little experience or expertise.

Complicated decisions require analysis and may result in a number of different possible outcomes. They’re predictable but require expertise to arrive at the answer.

Complex decisions involve cause and effect, but it’s not obvious what they are or how they are related – there is a great deal of unpredictability and the outcome can only be deduced in retrospect.

Chaotic decisions are those where cause and effect are unclear and too confusing. The only way to deal with a chaotic decision is to take an action – any action – to try and reduce it to a complex decision.

Understanding these types of decision helps us to realise where machine learning sits. Because what machine learning can do, better and more consistently than people, is predict. If the answer or outcome to a situation is at all predictable, machine learning can respond, and in record time. On the flipside, machine learning doesn’t perform well in those decisions that are complex or chaotic, because they’re unpredictable. These are the kinds of situations that require action and creativity, which is where humans excel.

How one industry has put machine learning to work

The financial services industry has grasped this distinction perfectly, implementing machine learning to do the predictable financial analysis previously done by people.

Companies likes Betterment and Money Farm have introduced ‘robo-advisers’, algorithms that instruct clients and manage their portfolios, typically without any human interaction. They operate in the simple and complicated spaces, making decisions based on the analysis of trends and data with predictable outcomes. However, these businesses still have human financial advisors to step in when decisions become unpredictable. The Brexit referendum is a great example of this; Betterment suspended algorithmic trading for two and a half hours after the unexpected result sent shockwaves through financial markets.

The robot analyst

Now we understand what machine learning excels at and how similar industries are applying it to their businesses, let’s look at how it applies to marketing and our original issue. Too much data, in too many places.

Until now, the marketing industry has tried to combat this by developing more analytics software – only adding to the problem. What we need is more analysis NOT more analytics.

In the finance industry, they have robo-advisors, in marketing, why not robo-analysts? They could perform the numerous repetitive, process-driven and predictable tasks that marketers do every week, such as data collection, traffic analysis and trend analysis. But when an unpredictable situation arises, such as Google unexpectedly changing the way search works, a pivot in business direction or a disgruntled customer, nothing can substitute human interaction.

It’s a combination of expertise and AI that can supercharge our marketing. With machine learning and AI to query and report on masses of data, marketers can stay informed of multiple threats and opportunities, whilst spending time on the creativity and implementation needed to exploit them.

This is why Vertical Leap developed Apollo Insights; our machine learning platform capable of those tasks that can be automated. Apollo audits every page of a website, checking every backlink and checking every query against every page, every week, AND filters data to spot new opportunities for growth. That’s more than is humanly possible, but with robots, algorithms and AI, we can operate at scale, making sure our focus is not too narrow.

The future of search marketing

We’ll be hearing a lot about automation, AI and machine learning. There are already discussions about whether search marketing can be completely automated.

Many of the things we do manually may be replaced and done more effectively by robots, but by then we will have evolved further. Search engines will evolve, user behaviour will be different, the way we seek out information will change – marketers will need to adapt with the changes and the only way to keep up will be to look for algorithmic enhancement.

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Chris Pitt is head of marketing at Vertical Leap and has over 20 years’ experience in sales and marketing, previously holding senior roles in tax and financial companies, working with customers such as Ernst & Young, Deloitte, KPMG and Groupama. A regular at exhibitions and events across the country, Chris has presented at all the major industry exhibitions as well as providing educational talks at Google’s London HQ.